Skip to main content
SpringerLink
Log in

A random walk based multi-kernel graph learning framework

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Graph learning is an important approach for machine learning. Kernel method is efficient for constructing similarity graph. Single kernel isn’t sufficient for complex problems. In this paper we propose a framework for multi-kernel learning. We give a brief introduction of Gaussian kernel, LLE and sparse representation. Then we analyze the advantages and disadvantages of these methods and give the reason why the combine of these methods with random walk is efficient. We compare our method with baseline methods on real-world data sets. The results show the efficiency of our method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Azran A, Ghahramani Z (2006) Spectral methods for automatic multiscale data clustering[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE. 1:190–197

  2. Bach FR, Lanckriet GRG, Jordan MI (2004) Multiple kernel learning, conic duality, and the SMO algorithm[C]//proceedings of the twenty-first international conference on machine learning(ICML '04): 6-14

  3. Belhumeur PN, Hespanha JP, Kriegman D (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  4. Bengio Y, Delalleau O, Le Roux N (2006) Label propagation and quadratic criterion. Semi-Supervised Learning 10:1315–1634

    Google Scholar 

  5. Cheng B, Yang J, Yan S et al (2010) Learning with-graph for image analysis. IEEE Trans Image Process 19(4):858–866

    Article  MathSciNet  MATH  Google Scholar 

  6. Donoho D (2004) For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution. Commun Pure Appl Math 59(7):797C829

    Google Scholar 

  7. Gonen M, Alpaydin E (2011) Multiple kernel learning algorithms. J Mach Learn Res 12:2211–2268

    MathSciNet  MATH  Google Scholar 

  8. Karasuyama M, Mamitsuka H (2013) Manifold-based similarity adaptation for label propagation[C]. Advances in Neural information processing systems(NIPS'13): 1547–1555

  9. Kumar A, Daum H (2011) A co-training approach for multi-view spectral clustering [C]//Proceedings of the 28th International Conference on Machine Learning(ICML-11) 393–400

  10. Kumar A, Rai P, Daume H (2011) Co-regularized multi-view spectral clustering[C]. Adv Neural Inf Process Syst (NIPS'11): 1413–1421

  11. Liu J, Chen Y, Liu M et al (2011) SELM: semi-supervised ELM with application in sparse calibrated location estimation. Neurocomputing 74(16):2566–2572

    Article  Google Scholar 

  12. Lov´asz L (1993) Random walks on graphs: a survey. Combinatorics, Paul Erdos is Eighty 2(1):1–46

  13. Ng AY, Jordan MI, Weiss Y (2010) On spectral clustering: analysis and an algorithm. Adv Neural Inf Proces Syst 2002(2):849–856

    Google Scholar 

  14. Norris JR (1998) Markov chains[M]. Cambridge university press, Cambridge

    MATH  Google Scholar 

  15. Rakotomamonjy A, Bach F, Canu S et al (2008) Simple MKL. J Mach Learn Res 9:2491–2521

    MathSciNet  Google Scholar 

  16. Shawe-Taylor N, Kandola A (2002) On kernel target alignment. Adv Neural Inf Proces Syst 14:367

    Google Scholar 

  17. Sonnenburg S, Ratsch G, Schafer C et al (2006) Large scale multiple kernel learning. J Mach Learn Res 7:1531–1565

    MathSciNet  MATH  Google Scholar 

  18. Wang J, Yang J, Yu K et al (2010) Locality-constrained linear coding for image classification[C]. 2010 I.E. conference on computer vision and pattern recognition (CVPR’10), San Francisco, CA, 2010, pp. 3360–3367

  19. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1):37–52

    Article  Google Scholar 

  20. Wright J, Yang AY, Ganesh A et al (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  21. Yan S, Xu D, Zhang B et al (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51

    Article  Google Scholar 

  22. Zhang X, You Q (2011) An improved spectral clustering algorithm based on random walk. Frontiers of Computer Science in China 5(3):268–278

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Science, Jilin Institute of Chemical Technology, Jilin, 132022, China

    Wangjie Sun

  2. School of Public Health College, Jilin Medical University, Jilin, 132013, China

    Shuxia Pan

Authors
  1. Wangjie Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuxia Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wangjie Sun.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, W., Pan, S. A random walk based multi-kernel graph learning framework. Multimed Tools Appl 77, 9943–9957 (2018). https://doi.org/10.1007/s11042-017-4599-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-4599-8

Keywords

Search

Navigation